Scaling Agentic Inference Across Heterogeneous Compute [Zain Asgar] - 757

2025-12-02 · Source: The TWIML AI Podcast with Sam Charrington · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Expert, extended

Summary

Gimlet Labs, co-founded by Zayn Asgar, focuses on optimizing AI inference workloads, particularly for agentic systems, aiming for 10x efficiency improvements. Initially targeting edge devices, Gimlet pivoted to data center-scale systems due to market demand and the applicability of their heterogeneous system stack. The company's technology orchestrates and runs models across diverse hardware, including Intel, AMD, and Nvidia GPUs, and Apple's M-series processors, to optimize unit economics and achieve best-in-class performance. Gimlet's approach involves a three-layer stack: workload disaggregation for fine-grain partitioning and cost-optimized hardware allocation, a compilation system to lower workloads to target hardware, and an autonomous kernel optimization layer that uses LLMs to generate and test highly optimized compute code. This strategy addresses challenges like networking heterogeneity and aims to maximize hardware utilization, leading to significant TCO benefits for customers, with current revenue in the eight figures from self-hosted data center deployments.

Key takeaway

For CTOs and VPs of Engineering managing large-scale AI inference, Gimlet's approach to heterogeneous hardware orchestration offers a compelling path to significantly reduce cost per token and improve performance. You should evaluate solutions that disaggregate workloads and dynamically optimize across diverse compute resources, including older or non-Nvidia hardware, to maximize utilization and achieve substantial TCO advantages without requiring a full rip-and-replace strategy.

Key insights

Heterogeneous inference orchestration across diverse hardware significantly improves AI workload efficiency and cost-per-token.

Principles

• Disaggregate workloads for optimal hardware allocation.
• Automate kernel optimization with LLMs for diverse hardware.
• Maximize hardware utilization to reduce TCO.

Method

Gimlet's three-layer stack disaggregates agentic workloads, compiles them for heterogeneous hardware, and autonomously optimizes kernels using an LLM-driven, hardware-in-the-loop system for performance and correctness.

In practice

• Utilize Kubernetes Dynamic Resource Allocation (DRA) for fine-grain GPU partitioning.
• Employ RDMA over Converged Ethernet (RoCE) for efficient inter-accelerator data transfer.
• Consider mixing older, lower-cost hardware (e.g., Intel Gaudi) with high-end GPUs for TCO benefits.

Topics

• Heterogeneous Inference
• Agentic AI Workloads
• Kernel Optimization
• Dynamic Resource Allocation
• LLM-driven Code Generation

Best for: Machine Learning Engineer, CTO, VP of Engineering/Data, AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by The TWIML AI Podcast with Sam Charrington.